Designing the Conceptual Landscape for a XAIR Validation Infrastructure : Proceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany

Fadi Al Machot

ISBN 10: 3031892739 / ISBN 13: 9783031892738

Published by Springer Nature Switzerland, Springer Nature Switzerland, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.

Seller Inventory # 9783031892738

Bibliographic Details

• Title: Designing the Conceptual Landscape for a XAIR Validation Infrastructure : Proceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany
• Publisher: Springer Nature Switzerland, Springer Nature Switzerland
• Publication Date: 2025
• Language: English
• ISBN 10: 3031892739
• ISBN 13: 9783031892738
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 312 grams

About this title

Synopsis

This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.
Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.
Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.